Dysfunctional paraspinal muscles in adult spinal deformity patients lead to increased spinal loading
Bibliographic record
Abstract
PURPOSE: Decreased spinal extensor muscle strength in adult spinal deformity (ASD) patients is well-known but poorly understood; thus, this study aimed to investigate the biomechanical and histopathological properties of paraspinal muscles from ASD patients and predict the effect of altered biomechanical properties on spine loading. METHODS: 68 muscle biopsies were collected from nine ASD patients at L4-L5 (bilateral multifidus and longissimus sampled). The biopsies were tested for muscle fiber and fiber bundle biomechanical properties and histopathology. The small sample size (due to COVID-19) precluded formal statistical analysis, but the properties were compared to literature data. Changes in spinal loading due to the measured properties were predicted by a lumbar spine musculoskeletal model. RESULTS: Single fiber passive elastic moduli were similar to literature values, but in contrast, the fiber bundle moduli exhibited a wide range beyond literature values, with 22% of 171 fiber bundles exhibiting very high elastic moduli, up to 20 times greater. Active contractile specific force was consistently less than literature, with notably 24% of samples exhibiting no contractile ability. Histological analysis of 28 biopsies revealed frequent fibro-fatty replacement with a range of muscle fiber abnormalities. Biomechanical modelling predicted that high muscle stiffness could increase the compressive loads in the spine by over 500%, particularly in flexed postures. DISCUSSION: The histopathological observations suggest diverse mechanisms of potential functional impairment. The large variations observed in muscle biomechanical properties can have a dramatic influence on spinal forces. These early findings highlight the potential key role of the paraspinal muscle in ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".